Several concerns have surfaced around the societal impact of computer vision models [1]}, to name a few:
mis-classification of people’s membership in social groups (e.g., gender) [2]}, [3]}, computer vision systems that reinforce harmful stereotypes [4]}, [5]} and the gender biases towards darker-skinned people [6]}.
Further, studies [7]} show that training on ImageNet might lead to potential biases and harms in models, that are then transferred to the downstream tasks that model is applied on.
Dulhanty and Wong [8]} studied the demographics on ImageNet, showing that males aged 15 to 29 make up the largest subgroup.
Stock and Cisse [9]} have shown that models trained on ImageNet exhibit mis-classifications consistent with racial stereotypes.
De Vreis  [10]} showed that the ImageNet trained models lack geographical fairness/diversity and work poorly on images from non-Western countries.
Recently, effort has been made by Yang  [7]} to reduce these biases by removing 2,702 synsets (out of 2,800 total) from the person subtree used in ImageNet.
Motivated by the importance of building socially responsible models, we follow recent works [12]} to systematically study the fairness, harms and biases of our models trained using self-supervised learning on random group of internet images.
